suicide , culture & e agriculture in rural india

Contradictions inthe Last Mile:Suicide, Culture,and E-Agriculturein Rural India

Glenn Davis Stone1

AbstractDespite its use to exemplify how the world is ‘‘flat,’’ India is in many ways‘‘spiky.’’ Hyderabad is a prosperous hub of information–communicationtechnology (ICT) while its impoverished agricultural hinterland is bestknown for dysfunctional agriculture and farmer suicide. Based on the beliefthat a lack of knowledge and skill lay at the root of agrarian distress, the‘‘e-Sagu’’ project aimed to leverage the city’s scientific expertise and ICTcapability to aid cotton farmers. The project fit with a national surge of ‘‘lastmile’’ projects bringing ICT to the village, but it was unique in using ICT toconnect farmers directly with agricultural scientists acting as advisors. Suchprojects fit the interests of many actors, which has led to an unrealisticnational enthusiasm about their impacts. This article uses the first five yearsof the project as a lens to view the cultural nature of both indigenous agri-cultural knowledge and ‘‘scientific’’ agricultural advising. Unlike lay publicswhose uptake of science is better known, with farmers the invention and

1 Washington University, St. Louis, MO, USA

Corresponding Author:

Glenn Davis Stone, Prof. of Anthropology and Environmental Studies, Washington University,

St. Louis, MO 63130

Email: [email protected]

Science, Technology, & Human Values000(00) 1-32

ª The Author(s) 2010Reprints and permission:

sagepub.com/journalsPermissions.navDOI: 10.1177/0162243910374808

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1

adoption of agro-scientific knowledge is deeply embedded in daily produc-tive activities and sociocultural interactions. E-Sagu eventually had to aban-don its construction of agricultural science as objective and acultural,resorting to rural methods of persuasion. It also found that it could onlysurvive by joining forces with companies promoting commodification ofagricultural inputs, which was a cause of the agrarian distress it sought toalleviate.

Keywordsindigenous knowledge, Internet, information–communication technology,agriculture, commodification, suicide, cultures of science

Despite being a prime example of how the world is supposedly ‘‘flat’’

(Friedman 2005), India is in many ways better characterized as exceedingly

‘‘spiky.’’1 In terms of technology and economic opportunity, few spikes are

as sharp at Hyderabad and its hinterland. ‘‘Cyberabad,’’ with its glamorous

film industry, and renowned universities and prosperous Hi-Tec City, is a

principal hub of India’s information–communication technology (ICT)

sector (Grondeau 2007) and an exemplar of ‘‘postindustrial high-tech

capitalism’’ (Sunder Rajan 2006). It is rapidly evolving, highly successful,

and filled with enough opportunity to exert a reverse brain drain

(Rai 2006). However, one need travel only fifty miles to rural areas like

Warangal District (figure 1), where desperately poor farmers till fields

with wooden plows as their kids sit in decrepit underfunded schools. Here,

women’s literacy rates are among the lowest in the country (less than half

of urban rates; Velkoff 1998) and upwardly mobile parents dream of their

children getting out.

In Warangal, the main economic engine—agriculture—is in perpetual

crisis: India’s ‘‘Achilles’ heel’’ (Sengupta 2006). The primary cash crop

is cotton, but yields are among the world’s lowest, and growers in recent

years have descended into a destructive spiral of escalating use of toxic

insecticides, resistant cotton pests, and mounting debts. In 1998, matters

came to a head in rural Warangal: After particularly severe bollworm

attacks and poor harvests, several hundred farmers took their own lives

(Stone 2002). The suicides were mostly small and marginal farmers (Reddy

and Rao 1998) and they mostly did it by drinking insecticides. Arguments

over the causes of the suicides were sometimes bitter (Stone 2005).

Monsanto, hoping to market genetically modified seeds that killed bollworms,

blamed the bollworms.2 Vandana Shiva, the antiglobalization activist, blamed

2 Science, Technology, & Human Values 000(00)

2

globalization (Shiva 2004; Shiva and Jafri 1998). Some government officials

attempted to medicalize the problem by suggesting a wave of clinical depres-

sion, and a Hyderabad NGO started a program for suicidal farmers, intending

‘‘to calm them through anti-depressant drugs’’ (Mathew 2005).

An important and different perspective on the suicides was manifested

a few years later by the appearance of an unusual facility in Oorugonda

village (figure 1), in the part of Warangal with the highest commitment

to cotton and the highest rate of suicide (Reddy and Rao 1998). Opening

in Spring 2004, this small office, bristling with computer terminals and digi-

tal cameras, marked the beginning of e-Sagu (sagu meaning cultivation in

Telugu), a project designed to aid cotton farmers by connecting them to

agricultural experts in a university lab in Hyderabad’s Hi-Tec City. The

idea for such an intervention had been gestating since 2002, when a profes-

sor of computer science named P. K. Reddy had envisioned a system in

which ‘‘agricultural experts will get the recent status of the crop through

[the] internet in the form of text and images, and they will generate appro-

priate advice which will be sent to the farmers through [the] internet . . . ’’

(Deccan Chronicle 2002). Reddy and colleagues wrote that

[The] majority of the farming community is not getting upper bound yield,

despite successful research on new agricultural practices, crop cultivars, crop

cultivation and pest control techniques . . . The term upper bound yield refers

to the yield that could be obtained using proper cultivation methods subject to

Figure 1. Map of India and Andhra Pradesh.

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advances in agricultural technology at that time. The upper bound yield may

change with progress in agricultural research. One of the reasons is that the

appropriate and timely scientific advice about farming is not reaching the

farmers (Reddy and Ankaiah 2005).

Explicitly identifying the farmers’ lack of information as a cause of

agrarian distress, the authors cited articles on farmer suicide. E-Sagu leaders

have continued to espouse this view in the popular press (e.g., Deccan

Chronicle 2006).

The premise that suicide may be linked to information deficits is not fan-

ciful; indeed, distortions in the information environment have emerged as a

key theme in recent anthropological research in Warangal (Stone 2002,

2007a). The question is if and how the delivery of external science could

mitigate the problem. Projects to aid local agriculture by delivering external

scientific expertise have a fascinating history, reflecting a range of histori-

cally contingent attitudes toward science and agricultural practice. In the

United States, when the Country Life Commission advocated bringing

modern agricultural science to farmers in 1909, it was reflecting an ascen-

dant ideology of ‘‘scientific’’ (Fordist or Taylorist) production. However, in

that case, the problem was getting the scientific advice to the farmers; the

head of the commission advocated a far-fetched model in which plant

doctors, breeders, and soil and spraying experts would ‘‘be established in

the open country’’ to guide the farmers (Busch 2000; Marglin 1996). In con-

trast, e-Sagu was designed around—in fact, was inspired by—the availabil-

ity of an excellent delivery mechanism: the use of advanced ICT to rescue

cotton farmers seemed the perfect intervention for spiky Andhra Pradesh.

In ICT circles, ‘‘the last mile’’ refers to the final leg connecting local cus-

tomers to electronic communication; by 2004, there were various last mile

projects delivering information to farmers on markets, monsoons, and new

products (Das 2000), and iconic images of ICT reaching the farm were

commonplace (figure 2). What distinguished e-Sagu was its plan to take

connectivity the next step, linking the farmer not just to information on the

Internet but to agricultural experts telling individual farmers how to farm.

Its basic conception, which resonated richly across a range of stake-

holders, was that farmers were foundering for lack of access to guidance

from agricultural scientists; forging this connection was a technical chal-

lenge, to be solved by ICT.

From the beginning, signs indicated the project to be a major success. It did

connect farmers to Hyderabad-based agricultural consultants, and it soon

announced findings that it had improved productivity and saved client farmers


4

Figure 2. An example of information–communication technology (ICT) agricultureimagery dating to the time of e-Sagu’s opening. The caption reads: ‘‘Indian farmersneed timely inputs and value-added dynamic information... The role of informationand communications technology (ICT) to bring about ‘‘digital inclusion’’ is critical tobroaden the knowledge-base of farmers . . . .’’ Printed with permission from THEHINDU, Chennai, India.

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thousands of rupees per year. In short-order, it won the best CSI-Nihilent

e-Governance Project Award; the ‘‘Fellows of Manthan Award’’ by Digital

Empowerment Foundation; a diploma for excellence in the use of information

technology in economic development by Stockholm Challenge; recognition

as a global example from Taiwan’s Ministry of Economic Affairs; and recog-

nition as one of the twenty ‘‘best technology users in India’’ by Business Today

(Business Today 2006). It soon announced plans to spread to other villages, and

to begin charging for its service.

Yet, on closer inspection, e-Sagu’s success story may have been more a

reflection of what people wanted to hear than what was supported by evi-

dence. As we see below, the evidence of positive impacts on farmers’ prof-

its was dubious, and the project’s impact on cultivation practices may prove

to be quite ironic. What the e-Sagu project does provide is an interesting

case study in the intersection of knowledge, technology, and culture in agri-

culture. This is a timely area for examination because agriculture, having

transformed from a form of production running largely on labor (Netting

1993) to one running mainly on imported material (fertilizer, pesticides, and

machinery), is now rapidly evolving into a productive system running on

information (Cash 2001, 431). This has brought a sea change toward

increasingly research-intensive agricultural technology (Kenney 1986)

and a growing commodification of agricultural information. Although the

Country Life vision of a corps of rural advisors never materialized, by the

late 1900s, U.S. farmers were becoming major consumers of data, consult-

ing, and other information (Wolf 1998; Wolf and Zilberman 2001). Inte-

gration of these new information technologies into agriculture clearly

brings about new structural relationships between farm operations and

other actors and institutions in what have been called new ‘‘social rela-

tions of information capitalism’’(Bousquet 2003). The particular issue at

stake are the special obstacles encountered by ‘‘boundary organizations’’

(Guston 1999) in mediating agricultural science and agricultural practice.

I show that conveying generic preexisting information to farmers—as

other e-agriculture interventions had done—is a qualitatively different

project than generating customized advice for farmers. Scholarship on

boundary organizations has generally lumped the kinds of information

being handled (e.g., Cash 2001), just as e-Sagu leaders saw the move from

providing generic to customized information as an unproblematic

improvement and logical progression. However, agricultural advising

engages local decision making in a way that produces a richly social

encounter on multiple levels. E-Sagu planners encountered this as soon

the ‘‘agricultural scientists’’ being sought as advisors turned out to be a


6

heterogeneous population with deep sociopolitical clefts. E-Sagu’s

boundary activities then collided with the complex nature of agricultural

decision making, which admixes environmental observation with social

mechanisms for making collective sense of crops and for selecting sources

of agricultural cues. Within four years, rather than transforming local agri-

culture, e-Sagu itself had been transformed as it struggled to recognize and

manipulate the social drivers of agricultural performance.

Equally revealing of the new ‘‘social relations of information

capitalism’’ were the partnerships e-Sagu was forced into in order to remain

solvent. E-Sagu, originally conceived as a boon to suicidal small and

marginal farmers, was eventually transformed into an agent of information

commodification, partnered with vendors of commodified inputs that had

been implicated in the farmers’ plight.

Why this analysis of e-Sagu’s impact so sharply contradicts the consen-

sus on its triumphs is a productive question in itself. I show that the

acclaim for the project reflects the wide roster of actors with vested inter-

ests in e-agriculture more than the empirical reality. The next section

explores this set of interests as it establishes the context of the rise of

e-agriculture in India.

The Popularity and Politics of E-Agriculture

India seemed to emerge as an ICT powerhouse just in time for the rescue of

backcountry agriculture. Between 1991 and 2006, the Indian software

industry grew from $150 million to almost $23.6 billion (Kuriyan and Ray

2007); in the early 2000s, when e-Sagu was conceived, ICT was growing at

an annual rate of 37 percent (Carmel 2003; Harris and Rajora 2006). The

conviction that ICT was a key to agricultural (and, more generally, rural)

development arose in several Asian nations around this time (Harris and

Rajora 2006), but nowhere did it have as much traction as in India. This

was not only because of India’s extraordinary ‘‘spikiness’’ but because

e-agriculture fits the interests of a particularly wide set of actors in India,

and those interests help explain its rise and positive reception. To the press,

it offers stories of dhoti-clad farmers getting commodity prices at the Inter-

net kiosk, irresistible to publications such as Business World (Kohli 1999),

Bloomberg News (Sharma 2007), the New York Times (Waldman 2004), and

the Wall Street Journal (2008). To government officials, it exemplifies

enlightened and effective public policy (Grondeau 2007). In 2000, the

Indian central government established a Working Group on Information

Technology for Masses, and in 2003, announced a national mission to bring

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IT applications to rural development and agriculture. The e-Sagu project

was funded primarily by the government of India through a venture entitled

Media Lab Asia. Several state governments also launched ‘‘last mile’’ pro-

grams (Kuriyan, Ray, and Toyama 2008). Andhra Pradesh was particularly

aggressive in promoting such ICT projects in the early 2000s, led by its

charismatic pro-globalization Chief Minister Chandrababu Naidu (The

Hindu 2007); Naidu had already attempted to launch a statewide Geo-

graphic Information System (GIS) project to optimize the use of ‘‘every

inch of land’’ in Andhra Pradesh (Business Line 2000). To ICT corpora-

tions, it provides an opportunity to burnish civic credentials and penetrate

new markets. For instance, Microsoft, with much fanfare, gave an award

to Delhi students who designed a farmer ‘‘Infostation’’ driven by Win-

dows CE (Parthasarathy 2005); in 2006, they launched the ‘‘Digital-

Green’’ project to disseminate agricultural information via digital video.

These corporations are also interested in developing new channels for sell-

ing agricultural inputs (a consideration likely to dominate the future

development of e-Sagu, as discussed in the final section). To the ICT

scientists setting up the systems, it offers a highly fundable and publish-

able topic (e.g., Ratnam, Reddy, and Reddy 2005) along with profes-

sional kudos. To academic institutions, it offers invaluable press

showing a development-conscious country the practical value of campus

research. The International Institute for Information Technology (IIIT),

which houses e-Sagu, has enjoyed glowing newspaper articles and TV

spots; it has also produced its own self-congratulatory media, including

a film in which the director of IIIT cites e-Sagu as an example of how the

institution ‘‘benefits the common man’’ and a narrator claims it to have

dramatically turned the Warangal landscape green through its ‘‘amazing

impact’’ (IIIT 2005).3 To academic business writers and free market

enthusiasts, it offers an exemplary case of profit-driven solutions to rural

poverty. Farmer Internet kiosks provide the most striking example of

the much-discussed ‘‘ICT4D’’ (ICT for Development), extolled by writ-

ers such as C. K. Prahalad (2004) as the means to ‘‘eradicate poverty

through profits’’ by selling advanced technological services to those at

the ‘‘Bottom of the Pyramid.’’ Finally, to some nongovernmental

organizations (NGOs), it offers a high-profile rural intervention.

An agriculture-oriented Warangal NGO played a key role in designing

e-Sagu and also performed its yearly evaluations (which were unfailingly

positive). An interesting feature of such ‘‘last mile’’ interventions is that

they appeal to both agricultural corporations and NGO’s with anticorpo-

rate orientations.


8

Given this convergence of disparate interests, it is not surprising that

e-agriculture projects (and great enthusiasm for them) spread through

India from the late 1990s on. Early initiatives, such as the National Virtual

Academy for Rural Prosperity begun in Pondicherry in 1998, established

‘‘Experimental Information Village Projects’’ offering farmers weather

reports and market prices (MSSRF 2007). In Gujarat, ‘‘telecentres’’

provided dairy farmers with information on cattle rearing; in both

Maharashtra and Tamil Nadu, Internet kiosks offered scheduling and mar-

ket information to sugarcane growers (Kohli 1999). In Madhya Pradesh,

the Gyandoot-Dhar project provided villagers with access to agricultural

commodity rates through cyber kiosks (Sreekumar 2007). The most ambi-

tious e-agriculture project has been ITC’s ‘‘e-Choupal’’ project (figure 3;

Harris and Rajora 2006). Featured prominently in the New York Times

(Waldman 2004), this initiative had set up 6,500 Internet kiosks by

2008 called e-Choupals (choupal meaning gathering place in Hindi) that

claimed to serve 40,000 villages while ‘‘empowering’’ four million farm-

ers in nine states of India (ITC 2009).4

From the Gujarati telecentres and Gyandoot kiosks to the nationwide

e-Choupals, the principal benefit of these ‘‘last mile’’ projects appears to

have resulted from their delivery of market information that helps farmers

reduce the power of intermediaries in the commodity chain. In evaluating

the success of e-Choupals, Upton and Fuller (2003) concluded that its

‘‘effective methods of price discovery, honest trading, and information

sharing’’ helped farmers escape the role of price-takers, while Prahalad

(2004) noted that online access to the latest crop prices enabled farmers

Figure 3. Web site display for ITC’s e-Choupal project.

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to avoid the exploitation of local purchasing monopolies and increase

incomes. Balaji et al. (2004, 41) too found that in Pondicherry ‘‘village

knowledge centers,’’ it was access to grain prices that mainly benefited

farmers. Broader impacts, such as opening economic opportunities to

marginalized groups, have yet to materialize (Sreekumar 2007).

Expertise: Taking Connectivity to the Next Step

With no shortage of apparently promising e-agriculture projects, e-Sagu

entered a crowded field when it opened its Oorugonda field office in

2004. E-Sagu’s founders distinguished their project by critiquing existing

e-agricultural interventions for failing to provide scientific management

and tailored advice:

[They] mostly move around marketing aspects, input supplies, price informa-

tion, contract farming etc. They cover only selected aspects and little atten-

tion is paid in the provision of advices [sic] on all aspects personally to the

farmers. In view of the shifting cropping pattern towards commercial and spe-

cialized crops, there is an increasing need for an integrated and scientific

management of these crops and to ensure this, an intensive and personalized

information flow to the farmers is essential. It is in this background, IIIT,

Hyderabad, India has developed a model using ICTs for providing persona-

lized information (Reddy et al. 2005b).

Reddy’s critique portrayed agricultural advising as the logical next step in

‘‘last mile connectivity.’’ Instead of eliminating intermediaries in the com-

modity chain, e-Sagu inserted a new player into primary production: the

agricultural scientist. Before 2004, there had been only fledgling attempts

at customized advice.5 E-Sagu recognized direct advising to be a different

undertaking, but saw its challenges to be essentially technical and logistical,

centered on the development of communication infrastructure and the hir-

ing of a staff of agricultural scientists.

The communication infrastructure that e-Sagu pioneered in 2004 began

with a field office in Oorugonda with three computers and a staff of local

‘‘coordinators,’’ each armed with a digital camera and a rudimentary knowl-

edge of agronomy. The project recruited 1,051 local farmers to participate

on a no-fee basis. Their cotton fields were visited weekly by coordinators

who photographed and recorded agroecological details (figure 4). The

images and information went onto CDs that were carried by a staffer by bus

or train to the e-Sagu lab at IIIT in Hyderabad several times each week


10

(bandwidth and phone connections did not allow direct Internet transmittal).

At IIIT, there was a dedicated lab with ten networked computers managing

the project with an Oracle-like database. The project’s ‘‘agricultural

experts’’ (discussed below) would view the field updates and then post

advice on the e-Sagu Web site, which could be accessed by the coordinators

and relayed to the farmers. Their advice included not only identification of

specific problems and solutions (e.g., pest identifications and recommended

sprays and fertilizers) but general management advice (e.g., on crop rota-

tions). In this way, the farmer, according to the e-Sagu Web site (e-Sagu

2004), ‘‘cultivates like an agricultural scientist.’’ The e-Sagu founder told

me after the farmers became ‘‘habituated’’ to receiving advice they would

probably be willing to pay for it.

This communication system seemed rational and impressive, but inter-

vention into the arena of farmer skill represents a major departure in more

important ways than these logistical considerations. Despite the superficial

similarity of electronic mediation, advising on how to farm makes for a qua-

litatively different kind of interaction than what occurred on the e-Choupals

and Internet kiosks. Those facilities served as simple conduits for indepen-

dently generated information such as the current market price for cotton and

the predicted arrival of the monsoon. Van der Meer et al. (2003) term this

‘‘dumb information,’’ but the key is that it is generic information indepen-

dent of the conduit—particularly market pricing, which accounts for the

Figure 4. Left: A coordinator at the Oorugonda office photographs a clientfarmer’s cotton field. Right: The IIIT office in Hyderabad with three agriculturalexperts.

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most convincing evidence of ‘‘success’’ in these facilities. In the few cases

where ‘‘last mile’’ projects had dabbled in actual advising, there had been

problems: assessments of these projects found them to be performing

poorly, plagued mostly by problems stemming from the burden of generat-

ing custom information. Even if the information were readily available,

farmers complained that it came in unhelpful forms and often not even in

their native language (Kuriyan and Toyama 2007, 4). However, the

problems run deeper than the communication model and language use;

I posit that these interventions foundered on the sociocultural dynamics

of agricultural decision making, which we will see in the e-Sagu case.

The Sociocultural Nature of Agricultural Skill inWarangal

Despite the widespread belief in the need to tell Indian farmers how to farm,

and in e-Sagu’s ability to do the telling, the dynamics of agricultural instruc-

tion are poorly understood. When institutionalized, such instruction is

called agricultural extension, an enterprise that some see as in crisis across

the world (Davidson and Ahmad 2003). While the persistent failures of

extension are often attributed to management and incentive issues (e.g.,

poor training, inadequate operational funds, lack of relevant technology,

top-down planning, centralized management, and absence of accountabil-

ity), more broadly its epistemological foundations have been questioned

because ‘‘it fails to adequately illuminate just ‘what happens out there’’’

(Davidson and Ahmad 2003). The uptake of scientific claims and advice

by farmers differs in important ways from the uptake of science by lay pub-

lics with which ‘‘public understanding of science’’ (PUS) scholarship is pri-

marily concerned. Farmers are not a lay public responding to science as

performed by scientists; agricultural technology and knowledge are what

farmers use, assess, and create daily in their profession. Farm management

is what they do. In contrast, Jasanoff’s (2005) ‘‘civic epistemologies’’

(national patterns in ways of public assessment of scientific knowledge

claims) concern scientific claims that the public may believe or doubt and

technologies they may admire, fear, buy, or boycott, rather than prescrip-

tions on how to ply one’s trade. Wynn’s (1996) analysis of sheep farmers’

response to scientific pronouncements on contamination concerns manage-

ment of a unique catastrophe rather than basic instruction on sheep farming.

In contrast, daily agricultural decision making poses a different set of prob-

lems for the penetration of outside scientific information and management


12

advice. I will note two issues of particular importance, the denial of which

was at the heart of e-Sagu’s model.

First is the dynamic nature of farm management and the complex matrix

within which agricultural decisions have to be made. Paul Richards

aptly characterizes farming as a ‘‘performance;’’ furthermore, it is an

inherently sociocultural enterprise, often requiring intricate fits between

agroecology and a range of local institutions (Lansing 1993; Richards 1989,

1993). Farmers do not simply acquire technical information on a seed or other

technology; they must try to develop the ability to perform with a technology

under changing conditions. This process is agricultural skilling, and

since technologies and markets and ecology and land and labor and ideas

change, it is necessarily an ongoing process (Stone 2007a)—a key difference

from the ‘‘skill’’ that is central to the literature on industrial deskilling

(Braverman 1974).

Second is the hybrid nature of the skilling process, which combines

ideas from off-farm (often institutional) sources with those generated and

manipulated by the local community. Agricultural skilling is also hybrid

in being generated by the interplay of environmental and social learning.

The influential model of Boyd and Richerson (1985) of cultural evolution

separates the processes of environmental and social learning, but agricul-

tural skill is rarely purely environmental or social. It is largely environ-

mental learning when, for example, a farmer sprays a pesticide and

observes its effect on a particular pest, but it is partly social as the choice

of spray, timing and method of application, and expectations and inter-

pretation of the results are all influenced by local farmers. It is largely

social learning when a farmer takes cues from whatever most neighbors

are doing, but this is partly environmental in that local consensus is

expected to embody results of past environmental learning. It is clearly

mixed environmental/social learning when a farmer heeds a neighbor’s

account of pesticide use or chooses to emulate a neighbor whose crop

seems to be doing well. Reliance on the social mechanisms such as copy-

ing neighbors or the local majority (‘‘conformist bias’’ in the literature of

behavioral ecology; Henrich 2001) is an adaptive strategy, particularly in

situations where environmental learning is difficult (McElreath 2004),

and so farmers should be expected to prefer locally derived information.

Agricultural skilling is thus a mostly local and sociocultural process,

even though it may incorporate non-local technology and information

(Brookfield 2001; Norgaard 1984; Scott 1998).6 What is often billed as

diffusion of an innovation from outside is actually a process of indigen-

ous adaptation or reinvention (Rogers 2003).

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However, the agricultural skilling process is not immune to serious

disruption, and the normally adaptive social components of learning may

have maladaptive effects. A disturbing finding of longitudinal research on

Warangal agriculture was agricultural deskilling in the cotton sector,

evidenced in farmers’ ‘‘indiscriminate and excessive’’ use of pesticides

(Reddy and Rao 1998; Vandeman 1995). It was also reflected in seed fads:

each village would have a new favorite seed every year, often a new seed on

the market, with farmers following each other’s lead with little knowledge

of the properties of the seeds they were buying (Stone 2007a). The root of

the problem was that the seed system lacked the consistency, recognizabil-

ity, and reasonable pace of change needed for agricultural skilling to occur.

The environmental component of skilling was disastrously impeded,

leaving an unusual and maladaptive reliance on pure social learning. This

was what was reflected in seed fads and in the misuse of pesticide and

the general dysfunction underlying cotton farmer suicides. This was

the situation that brought e-Sagu to town, offering ‘‘scientific’’ expertise

ostensibly decoupled from the social dynamics of skilling. However, even

before its encounter with the sociocultural complexities of agricultural

decision making, e-Sagu had its encounter with agricultural science.

Negotiating Agricultural Advice

The ‘‘upper bound yield’’ that e-Sagu sought for Warangal farmers was

understood to be obtainable by ‘‘using proper cultivation methods subject

to advances in agricultural technology at that time,’’ the result of ‘‘progress

in agricultural research’’ (Reddy and Ankaiah 2005). In 2003, as leaders of

e-Sagu began work on what they saw as the crux of the problem—the com-

munication infrastructure—they delegated the seemingly more bureaucratic

task of assembling the advising staff to two retired agricultural officials.

The staff was to comprise ‘‘agricultural experts:’’

Agricultural experts (AEs) are required to provide advice on factors that

affect crops and their effect on crop productivity. An AE possesses

advanced nontrivial knowledge about management of crops, and the exper-

tise to recommend possible steps based on the current crop situation. As a

result of intensive research, there is increasing knowledge regarding agri-

culture. Also, given a crop situation, we have a large pool of qualified agri-

cultural scientists to provide appropriate advice to the farmers (Reddy and

Ankaiah 2005).


14

Having animated the e-Sagu project from its inception, this vision of a ready

pool of authorities on an essentialized ‘‘agricultural science’’ of smallholder

production is an intriguing text in itself. In a general sense, it obviously

neglected the well-known social/cultural components in the construction

of scientific knowledge (e.g., Bloor 1991; Collins 1981; Knorr-Cetina

1981; Latour and Woolgar 1986). However, it also erased the political-

economic drivers in agricultural research that strongly shaped what

‘‘knowledge’’ was available and who was available to provide it.

First consider that the ‘‘intensive research’’ cited by Reddy and Ankaiah

has long been largely oriented mainly toward developing agricultural

inputs—pesticides, genetically modified seeds, and so on—and evaluating

their use—fertilizer response, pesticide dosages, and so on (Busch and

Lacy 1983; Busch, Burkhardt, and Lacy 1991). Analyses of the drivers of

this research show smallholder efficiency to be a low priority indeed

(Binswanger and Ruttan 1978; Busch, Burkhardt, and Lacy 1991). For an

illustration, one needs look no further than the fact that virtually all of the

cotton growing in Warangal is from hybrid seed. The shift to hybrid crops is

a widely known phenomenon of political biology, carving out a major niche

for agricultural capital and entraining state agricultural research as a devel-

opment arm of that capital (Kloppenburg 2004). The benefits of this tech-

nology to capital in the United States have been documented (Berlan and

Lewontin 1986; Fitzgerald 1990), as have some of its deleterious impacts

on farmer skilling (Fitzgerald 1993; Ziegenhorn 2000). However, impacts

have been much worse for Indian cotton farmers, exacerbated by the tan-

dem spread of unregulated cotton seed markets and hybrids which both con-

tribute to deskilling in cotton agriculture (Stone n.d.).

The research priority on commodified inputs occurs not just in the pri-

vate sector but also in Indian public agricultural research institutions that

are ostensibly tasked with catering to the needs of India’s overwhelmingly

smallholder farmers. Hybrid cotton was first developed at a public research

station in Gujarat, and state institutions still develop the parent lines used

in making hybrids, which find their way (legally or not) into the hands of

commercial breeders. These hybrid seeds are also fertilizer-intensive,

water-intensive, and most importantly, pesticide-intensive; most Indian cot-

ton seed is Gossypium hirsutum, the New World species lacking natural

resistance to many Asian insects and diseases (Kulkarni et al. 2009). Thus,

reliance on insecticide spread with cultivation of commercial hybrid cotton,

and Indian cotton farmers became classic victims of the ‘‘pesticide tread-

mill’’ as sprays killed off beneficial predator insects while pests have devel-

oped resistance. In short, the troubled agricultural decision-making system

Stone 15

15

that e-Sagu sought to correct with access to agricultural science was itself

blowback from agricultural science.

However, with its enormous population of science-educated citizens, its

thriving NGO sector, and its widely recognized problems in agricultural

sustainability, India has a significant alternative agriculture movement.

While most agricultural training and university programs take the heavy

reliance on external inputs as a given and pay scant attention to alternative

cultivation, other programs take these practices to be unsustainable and

advocate alternative cultivation philosophies. Such programs are offered

by international organizations such as Food and Agriculture Organization

of the United Nations (FAO) and by private specialized schools and in some

cases by public universities. The most common alternative cultivation

regime is Integrated Pest Management (IPM), which combines minimal use

of conventional pesticides with tactics such as early plowing, trap crops,

bird perches, scouting, and neem oil (Mancini, Van Bruggen, and Jiggins

2007). IPM does not completely avoid commodified inputs (practitioners

sometimes buy pheromone traps and parasitic wasps and may buy rather

than make neem oil), but it sharply reduces input costs. A few programs also

teach organic cultivation or NPM (non-pesticide management; Eyhorn,

Ramakrishnan, and Mader 2007). Graduates of these alternative agriculture

programs find work with NGOs, companies selling IPM/organic supplies

and consulting, or with organic cultivation projects.

Several alternative cotton cultivation programs have been run in

Warangal District in the past decade, including an IPM program

cosponsored by Novartis and a local NGO; an IPM field school sponsored

by FAO; and organic/NPM ventures. These projects varied in their

underlying philosophy and in how agricultural practices were presented

to farmers. One NPM project, run by a partnership of Indian and Dutch

firms, was entirely profit oriented, and it stressed to farmers the need for

organic certification to obtain a price premium. However, some NGO-run

programs may take on a messianic zeal. For instance, leaders of Modern

Architects for Rural India (MARI, a major NGO active in environment and

agriculture in Warangal District) exhorted farmers to shun pesticides in

cotton farming not out of material interests but because of benefits to the

environment and community. Participating farmers basically humored this

rhetoric and made scant use of the techniques after the project’s conclusion,

remaining broadly skeptical of non-pesticide practices.

Agricultural advice, then, is variable in content and philosophy, and it is

shaped by disparate groups who develop and pass on models of behavior—

in other words, it is largely sociocultural. It is not surprising that e-Sagu,


16

with its construction of an apolitical, homogeneous ‘‘agricultural exper-

tise,’’ would ignore these dynamics of the knowledge and practice. It did

not, at least in the beginning, even take a position on the cultivation philo-

sophy guiding its advising, and the two retired agricultural officials who

hired AEs were left to devise their own hiring criteria. Of the six experts

initially hired, one (the team leader) held a doctorate in plant pathology and

the rest held masters degrees in entomology, plant pathology, or agricultural

extension. None had significant experience in cultivation; the team leader

was not from a cotton-growing area and had received no practical training

in his education. These ‘‘experts’’ also varied in philosophy regarding

inputs: one was fresh from work on the idealistic MARI organic cotton proj-

ect and he tended to advise farmers to use alternatives to pesticides, while

others were more committed to high-input industrial cultivation. Which AE

was assigned to which farmer was normally random, but sometimes based

on which AE ‘‘knew the most’’ about an issue in the farmer’s field. Farmers

were not matched with AEs based on their own propensities regarding IPM

or conventional farming: that would have required confronting the sociocul-

tural nature of agricultural advice.

E-Sagu encountered complexities at the farmer end of its boundary func-

tion very quickly. From the outset, most client farmers in Oorugonda

showed an indifference to e-Sagu advice that was inconsistent with their

lacking only a conduit to gain access to the expertise they needed. While

the coordinators had conveyed hundreds of field forms and digital images

from the farms to the AEs, the farmers had showed a discouraging lack

of interest in the advice. When I met with a group of the AEs near the end

of its first season, they complained that the ‘‘farmers did not ask questions at

all.’’ This chagrined the AEs, one of whom lamented that ‘‘If those farmers

had cooperated we could have worked wonders.’’ In terms that echo the ear-

lier account of the cultural nature of agricultural knowledge, a respected

local crop breeder explained the farmers’ response to e-Sagu: ‘‘Most farm-

ers are not interested, not because of deficits in the system as a whole but

due to lack of faith in the persons giving advice. In general farmers receive

advice only from those in whom they have faith and from the person who

knew them and their village conditions.’’7

By summer 2007, the dynamics of the AE corps had changed in many

ways. Except for the team leader, the staff had completely turned over.

There was another change as well: The presumption that agricultural

experts were readily available, needing only an electronic conduit to the

farmers, had been replaced by the realization that e-Sagu would actually

have to train its own experts. Most of the ‘‘agricultural scientists’’ they

Stone 17

17

had assumed to be plentiful were either off doing research on inputs or

had received academic training that left them poorly equipped to tell

an actual farmer how to cultivate. In addition, the team leader had

immersed himself in learning about cotton cultivation, reading scientific

literature and learning from farmers and NGOs in the project areas. He

had six newly hired advisors working under him; he was now in charge

of hiring and he was busily training the recruits, aiming for greater

consistency in advising.

E-Sagu was also increasingly coming to represent a single cultivation

philosophy. Although keeping with the premise that agricultural expertise

is generic and available (lacking only a delivery mechanism), it had devel-

oped a definite anti-pesticide orientation. In its first year, e-Sagu experts

sometimes advised farmers to try nonchemical methods of combating pests

(e.g., trap crops, pheromone traps, scouting, and encouragement of benefi-

cial insects and birds), but more frequently they advised spraying the latest

pesticides. Subsequent interviews and e-Sagu reports indicate a movement

toward emphasizing IPM and Integrated Nutrient Management (INM). The

project’s in-house evaluation reports began to recognize IPM as a guiding

philosophy, even taking adoption of IPM methods as a measure of the proj-

ect’s success (Rao et al. 2006).

A key factor in these changes was the team leader’s increasing experi-

ence with cotton farmers and his growing conviction that the farmers’ heavy

pesticides use was their root problem. Since AEs were now receiving much

of their training at e-Sagu, it was possible to harmonize the cultivation phi-

losophy around the theme of reducing pesticide use, both through actual

training of AEs and daily interactions among the staff. In 2008, one senior

staff member told me that the farmers’ chemical philosophy was ‘‘a

Frankenstein that is destroying the environment.’’ The problem was that

it had not been shaped by the farmers’ culture of agriculture; on the con-

trary, the e-Sagu philosophy had developed partly as a corrective to the

farmers’ penchant for spraying insecticides in their perennial war with

pests; the e-Sagu staff was not only outside of the social nexus of agricul-

tural decision making, but it had staked out a position that was unpopular

among most farmers. Their advice developed little traction. In 2008, a

senior staffer told me bluntly that farmers continue to demand chemicals

to kill insects and that they generally regard e-Sagu’s recommendations

of IPM methods as ‘‘rubbish.’’ Thus, after starting with a paradigm of

perspective-free advice, e-Sagu came to endorse a specific approach to cot-

ton farming—unfortunately, a perspective that was incompatible with

entrenched local attitudes. Although there obviously were instances of AEs


18

diagnosing and recommending cures for agricultural problems,8 the larger

picture was not so much one of an electronic pipeline delivering desirable

information as one of a clash of cultures.

Shifting Definitions of Success

Learning how to manage a farm and advising others on running farms are

complex processes with major sociocultural components and this account

has shown how the beginnings of e-Sagu were more of an unproductive

collision of these sociocultural components than a productive transfer of

technical or skill. This account is clearly inconsistent with the widespread

recognition of e-Sagu’s success. Reports on and evaluations of ‘‘dumb

information’’ interventions have shown some signs of positive impacts; as

noted above, e-Sagu also claimed a record of glowing impact assessments

that were lauded in the press (Deccan Chronicle 2005a). The empirical

assessment clearly requires further attention.

Given the original conception of the project, the ultimate yardstick of

success would be a reduced farmer suicide rate, but this is impossible to iso-

late. Many factors affect suicide rates; the disputes noted above barely

scratch the surface (Stone 2007b). Even the suicide act itself is open to

interpretation: it may be seen as an escape, a desperate move to obtain a

cash payment for the family, or even as a communicative act (Kantor

2008). More to the point, the spatial and temporal dynamics of suicide

among Indian farmers are very poorly understood; for instance, in the years

following the well-publicized 1998 suicide spike in Warangal District there

were smaller spikes in Guntur and Kurnool Districts, none of which can be

clearly linked to local causes. Various developments also confound any

attempt to isolate effects on suicide rates; for instance, e-Sagu began oper-

ating around the same time that a new Congress Party administration was

elected to the state government claiming it would end farmer suicides.9

E-Sagu also coincided with the adoption of genetically modified cotton,

which has been blamed by some for suicide among cotton farmers.10

A less ambiguous assay would come from the comparative assessments

that e-Sagu commissioned. Reddy and Ankaiah (2005) assert that e-Sagu

advice raised yields 30–50% between 2003 and 2004,11 although offer no

information on how the samples were selected. In 2005, an evaluation was

conducted by Centre for Environmental Studies (CES), a small Warangal

NGO that had helped get the project started in Oorugonda.12 In this report,

Reddy et al. (2005) also compare farmers’ experiences in 2003 (data

collected prospectively) and 2004 (the first year of e-Sagu). They found

Stone 19

19

that while participants in all three e-Sagu villages lowered fertilizer

applications, two villages actually increased pesticide sprayings (although

at lower costs, an oddity that goes unexplained); they also found two

e-Sagu villages to have lower cotton yields in 2004. The authors admit that

the analysis is problematic because it relies on farmer’s recall of a whole

year of inputs, and it compares two years in an environment where

weather, pest attacks, and land cultivation patterns often vary sharply from

year to year (Reddy et al. 2005a).

CES researchers also asked for farmers’ perceptions of a range of issues,

including how useful e-Sagu advice had been and how it had affected

their inputs and yields. The perceptions of changes between the two years

belie the report’s own figures, as farmers claim that sprayings dropped and

yields rose. Still, Reddy et al. (2005b) used the farmer perceptions to claim

that e-Sagu clients saved an average of Rs. 3820 ($95) per acre of cotton—a

figure that has since been repeated in publications, presentations, Web sites

(e-Sagu 2004), newspaper articles (The Hindu 2006), and flyers distributed

to recruit farmers into the project. Total savings per farmer were claimed to

be Rs. 11,240 ($280).13 Reddy et al. (2005b) also compared e-Sagu farmers

with a control group from non-project villages, in which the e-Sagu group

had lower input costs and greater yields. Yet, the control group was ran-

domly selected while the e-Sagu group was biased toward the prosperous,

educated, and progressive farmers who would try such a service or be

recruited by it; these farmers were also more than twice as likely to access

newspapers, radio, and TV, and over ten times as likely to attend agricul-

tural exhibitions and farmer training camps.14

In short, the empirical basis for e-Sagu’s claims of positive impacts is

highly dubious. The narrative of success appears to result instead from the

appeal of e-agriculture in early millennium India, with its roster of vested

interests. However, Oorugonda farmers were unimpressed and they dropped

out in droves in the third year when e-Sagu instituted a fee (the funders had

insisted on the project becoming self-supporting). The fee was small—an

average of about under Rs. 500/year (around $12.50) for a project claiming

to provide Rs. 11,240 in economic benefit—yet less than a fifth of the

clients agreed to pay for continued advice. The Oorugonda office closed

after only two seasons. The farmers, an e-Sagu staffer lamented, had been

‘‘difficult to handle.’’

However, e-Sagu itself did not fold; indeed, by 2007, it was operating in

six new venues in Andhra Pradesh and there were hopes of going national.

However, there had been two interesting and somewhat contradictory

changes. First, the project had implicitly accepted that their advice would


20

not sell on its economic merits alone (their benefits analysis notwithstand-

ing). E-Sagu therefore began to involve itself in the very sociocultural

process of skilling it had denied in its conception of farmers merely needing

a conduit to scientific guidance. New e-Sagu venues were chosen where

the project had special powers of persuasion; for instance, one of the

new venues was in the home village of the IIIT professor who founded

e-Sagu. The Hyderabad staff began to visit fields themselves, to interact

more with the farmers and develop rapport. They began to offer cooperative

clients high praise and publicly award flower garlands. This development

represents an interesting inversion of the more common pattern, documen-

ted in PUS literature, of lay citizens learning the languages and cultures of

science to have the competence and credibility to influence medical/scien-

tific institutions (Epstein 1995, 1996). In the case of e-Sagu, ‘‘agricultural

experts’’ came to understand that agricultural decision making had a sub-

stantial sociocultural component that required them to adopt local practices

to have local credibility.

The second change results from the decision by government funders that

the academics’ job is developing technology and capacity, not retailing

agricultural management; e-Sagu itself would have to concentrate on devel-

oping training modules and computer solutions and leave the running of

advice centers to corporate or NGO partners. Given the continuing difficul-

ties in getting farmers to pay for e-Sagu advice, this meant that field offices

would need revenue-providing partners. For instance, the new e-Sagu center

in Malkapur, Warangal District, is jointly operated with an Indian conglom-

erate that sells agricultural inputs, and its office doubles as a seed, fertilizer,

and pesticide shop; it also offers loans (figure 5). The Andhra Pradesh state

government has been trying to establish a network of village Internet cen-

ters, and the future of the e-Sagu experiment is likely to be some form of

merger with this network. Eventually, there are expected to be dozens of

field offices operated by partners, most of whom will aim to sell goods

(such as pesticides) and services to farmers.

Conclusion

‘‘Everything eventually morphs into the way the world is,’’ as an organic

food executive assured Michael Pollan (2006, 152). The question, of course,

is just how the world is, with its new ‘‘social relations of information capit-

alism.’’ This look at one of the world’s spikiest landscapes, and at an attempt

to leverage that spikiness by saving suicidal farmers with advanced ICT, sug-

gests two answers. The first concerns the complex and fraught relationship

Stone 21

21

between the farmer and the seemingly inexorable forces of commodification.

After its first few years on generous government support, e-Sagu was increas-

ingly involved in commodification of farm inputs, both in its partnering with

input vendors and its increasingly serious attempts to get farmers to buy its

advice as a commodity. The need to develop hybrid commercial and nonpro-

fit organizational forms to keep Last Mile projects afloat has emerged as a

theme in India (Kaushik and Singh 2004). However, e-Sagu’s emergence

as an agent of commodification is ironic in this regard, given that its original

target audience was the small and marginal farmers who dominated the sui-

cide lists, who were thought to be the most bereft of technical information.

These are also the farmers least able to buy agricultural inputs; in fact, debts

to input vendors were the leading proximate cause of the suicides (Reddy and

Rao 1998).15 In this, e-Sagu is hardly unique. In Kerala, Kuriyan and cowor-

kers too found the Akshaya Project ran into a marked contradiction between

the goals of profitability and development; rural ICT kiosks struggled to turn

a profit, and kiosk operators catered to the middle-class customers. The rural

poor, who were the intended beneficiaries of the kiosks, were rare users

Figure 5. E-Sagu staff in the Malkapur office. They are posing in the outer roomwhich doubles as a pesticide shop. The office is also sponsored by a bank thatmakes agricultural loans.


22

(Karnani 2006; Kuriyan, Ray, and Toyama 2008). Echoes may even be found

in the U.S. Country Life project noted above, which was propelled largely as

mechanism to induce greater use of agricultural inputs.

But just as important, although probably less appreciated, is the lesson on

the cultural nature of agricultural knowledge among the farmers and the

‘‘experts.’’ By assuming an external scientific source of agricultural skill,

e-Sagu was unwittingly based on an extreme acultural model of external

scientific expertise. However, agricultural skilling in rural India is in many

ways a sociocultural process. This is not necessarily an impediment to

successful cultivation, but it poses a fundamental problem to an external

skilling system designed to bypass the skilling process and directly guide

the farmer to cultivate ‘‘like an agricultural scientist.’’ E-Sagu’s own evolu-

tion reflects this reality of the sociocultural nature of agricultural skill. This

is neatly encapsulated in the image of the e-Sagu ‘‘expert’’ publicly award-

ing a flower garland to a farmer who had followed their advice. The gar-

landing was entirely social, meant to encourage admiration of the e-Sagu

client, and the reason this was happening in this particular village was social

as well: this was the home village of the e-Sagu director, where the project

felt it would have great acceptance. The experts, meanwhile—originally

conceived as agents of an advanced agroscientific establishment lacking

only a conduit to the last mile—actually had to be trained by the managers

of the conduit, and trained into a particular philosophy that had evolved

locally in the e-Sagu office.

Notes1. Florida (2005) depicts ‘‘spikiness’’ using levels of nighttime light as a proxy for

economic production. However, agriculture, India’s largest productive sector, is

not indexed by light, nor is the spikiness that here refers to opportunity, access

to resources, and health of the economy.

2. See the articles on their corporate Web site (e.g., Times of India 1999) and state-

ments by Monsanto officials (Vidal 1999).

3. This institution has no connection with Indian Institutes of Technology (IIT),

India’s famous network of national universities, a branch of which was opened

in Hyderabad in 2008.

4. E-agriculture projects have continued to appear through the late 2000s; a recent one

is the NetHotzone program begun in May 2008 at Gujarat agricultural fairs, which

provided free Internet kiosks designed to help farmers get price information and to

‘‘bring about a sea change in agrarian economy’’ (ThaiIndian News 2008).

5. The Chennai-based n-logue project, which established fee-based Internet kiosks,

claimed to offer ‘‘expert advice on better farming techniques and solutions to

Stone 23

23

crop and animal diseases’’ along with government documents, astrology, and

weather and market information (n-logue 2007). However, n-logue users were

largely on their own to get advice via e-mail and videoconferencing. Sponsored

by agricultural inputs giant Nagarjuna Group (with backing from the World

Bank), the iKisan initiative established village technical centers operated by agri-

cultural graduates who mediated electronic resources for paying customers

(Meera 2002; Meera, Jhamtani, and Rao 2004). These operators had access to

agricultural databases they could use to look up material for farmers and, it was

claimed, provide advice where appropriate.

6. Dove’s (2000) account of the adoption and evolution of rubber cultivation by Indo-

nesian smallholders demonstrates how agricultural skilling involves a complex

negotiation of ecological constraints, economic demands, and social considera-

tions. The ‘‘successful production of rubber knowledge’’ among Kantu farmers

was an agricultural performance in which basic planting decisions were inextric-

able from scheduling, market involvement, land tenure, and gender relations. The

Kantu case also underscores how innovation adoption is less passive than assumed;

integrating an innovation into local practice can itself be highly innovative. For a

contrasting view on hybridity in agricultural knowledge, see Gupta (1998).

7. Some of the farmers were blunter. In one village near the e-Sagu project, a key

informant of mine is an educated and highly intelligent farmer reputed to be

‘‘scientific;’’ yet, he said he would have no interest in e-Sagu advice because

agricultural experts ‘‘all just say whatever they want’’ (Telugu: everikivachina-

tlu varu cheppinaru) and routinely disagree with each other.

8. A diagnosis by one of its staff led to e-Sagu being embroiled in controversy in its

first year. In 2004, there was an unusual mid-season of outbreak grey mildew, a

fungal disease that is normally a minor problem only late in the season. It was

diagnosed incorrectly by local and state agronomists, but correctly by the team

leader at e-Sagu, who eventually went to the press when he was unable to con-

vince any officials to endorse the correct treatment. This led to hostility between

e-Sagu and officials. This mildew outbreak was also seized by anti-

biotechnology groups, who blamed it on the new Bt cotton seeds. This claim was

repeated in headlines, even though a government study found the disease to affect

Bt and conventional cottons alike (Sharma 2005). Thus, by being right, e-Sagu

managed to alienate both the green NGO sector and the agronomic establishment.

9. Less than a year later, the chief minister conceded that there had been 1,289

farmer suicides since he took office, 679 of which were ‘‘genuine’’ (Deccan

Chronicle 2005b).

10. Those blaming suicide on GM cotton include Vandana Shiva (2008) and Prince

Charles (Lean 2008), although the empirical basis for the claim is dubious

(Gruere, Mehta-Bhatt, and Sengupta 2008).


24

11. The cotton season often straddles two calendar years: seeds are typically planted

in late June and may be harvested as late as March. To make the discussion less

cumbersome, I refer to cotton seasons by the year the crop was planted.

12. Disclosure: I have collaborated with this NGO in two surveys of Warangal

farming.

13. No measure of dispersion is provided, although this is probably more important

to the farmer than the mean (Stone 2007a).

14. This is hardly the only study to have been unable to clearly isolate impacts of

agricultural extension. Since it is unrealistic to have random treatment and con-

trol groups for farm management, participants are invariably a biased group. For

example, Sulaiman et al. (2005b) studied a Tamil Nadu on-farm extension proj-

ect, in which the self-selected or invited client farmers were significantly larger

and wealthier than non-clients.

15. This irony is not lost on the thoughtful scientists who started the project; the

founder of the project pointed it out wistfully to me in 2008.

Author’s Note

The author is grateful to P. Krishna Reddy and G. Syamasundar Reddy of e-Sagu

and A. Sudarshan Reddy of the Centre for Environmental Studies for their time and

insights. Invaluable logistical support and wisdom was provided by E. Rammohan

Rao. For comments and discussion, the author is grateful to Puneet Sahota and Pris-

cilla Song, although the author alone is responsible for any deficiencies in the

article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interests with respect to the authorship

and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research and/

or authorship of this article: This material is based upon work supported by the

National Science Foundation under Grant No. 0314404 and by the Wenner-Gren

Foundation for Anthropological Research.

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Bio

Glenn Davis Stone is Professor of Anthropology and Environmental Studies at

Washington University, St. Louis. His research centers on social, political, and

ecological aspects of agriculture. Much of his recent work has concerned agricul-

tural biotechnology.


32

suicide , culture & e agriculture in rural india

Documents

scientific knowledge

keywordsindigenous knowledge

terms of technology

agricultural scientists

lack of knowledge

poor farmers

cotton farmers

sagu project